Dynamic power management approaches based on neural network

Dynamic Power Management (DPM) is a technique to reduce power consumption of electronic system. by selectively shutting down idle components. In this article we try to introduce back propagation network and radial basis network into the research of the system-level policies. We proposed two PAY policies-Back propagation Power Management (BPPM) and Radial Basis Function Power management (RBFPM) which are based on Artificial Neural Networks (ANN). Our experiments show that the two power management policies greatly lowered the system-level power consumption and have higher performance than traditional Power Management(PM) techniques-BPPM is 1.09-competitive and RBFPM is 1.08-competitive vs. 1.79,145,1.18-competitive separately for traditional timeout PM, adaptive predictive PM and stochastic PM.

Cancer classification based on direction-basis-function neural networks

Automatic molecular classification of cancer based on DNA microarray has many advantages over conventional classification based on morphological appearance of the tumor. Using artificial neural networks is a general approach for automatic classification. In this paper, Direction-Basis-Function neuron and Priority-Ordered algorithm are applied to neural networks. And the leukemia gene expression dataset is used as an example to testify the classifier. The result of our method is compared to that of SVM. It shows that our method makes a better performance than SVM.

Low Power Integrated Circuits for Wireless Neural Recording Applications

A group of prototype integrated circuits are presented for a wireless neural recording micro-system. An inductive link was built for transcutaneous wireless power transfer and data transmission. Power and data were transmitted by a pair of coils on a same carrier frequency. The integrated receiver circuitry was composed of a full-wave bridge rectifier, a voltage regulator, a date recovery circuit, a clock recovery circuit and a power detector. The amplifiers were designed with a limited bandwidth for neural signals acquisition. An integrated FM transmitter was used to transmit the extracted neural signals to external equipments. 16.5 mW power and 50 bps - 2.5 Kbps command data can be received over 1 MHz carrier within 10 mm. The total gain of 60 dB was obtained by the preamplifier and a main amplifier at 0.95Hz - 13.41 KHz with 0.215 mW power dissipation. The power consumption of the 100 MHz ASK transmitter is 0.374 mW. All the integrated circuits operated under a 3.3 V power supply except the voltage regulator.

Multi channel micro neural probe fabricated with SOI

Extracellular neural recording requires neural probes having more recording sites as well as limited volumes. With its mechanical characteristic and abundant process method, Silicon is a kind of material fit for producing neural probe. Silicon on insulator (SOI) is adopted in this paper to fabricate neural probes. The uniformity and manufacturability are improved. The fabricating process and testing results of a series of Multi channel micro neural probes were reported. The thickness of the probe is 15 mu m-30 mu m. The typical impedance characteristics of the record sites are around 2M Omega at 1k Hz. The performance of the neural probe in-vivo was tested on anesthetic rat. The recorded neural spike was typically around 140 mu V. Spike recorded from individual site could exceed 700 mu V. The average signal noise ratio was 7 or more.

Implantable Microsystem for Wireless Neural Recording Applications

A prototype microsystem is presented for wireless neural recording application. An inductive link was built for transcutaneous wireless power transfer and data transmission. Total 16.5 mW power and 50 bps - 2.5 Kbps command data can be received over 1 - 5 MHz with a distance of 0-10 mm. The integrated amplifiers were designed with a limited bandwidth for neural signals acquisition. The gain of 60 dB was obtained by preamplifier at 7 Hz - 3 KHz. An integrated FM transmitter was used to transmit the extracted neural signals to external equipments with 0.374 - 2 mW power comsumption and a maximum data rate of 500 Kbps at 100 MHz. All the integrated circuits modules except the power recovery circuit were tested or stimulated under a 3.3 V power supply, and fabricated in standard CMOS processing.

SOC dynamic power management using artificial neural network

Dynamic Power Management (DPM) is a technique to reduce power consumption of electronic system by selectively shutting down idle components. In this article we try to introduce back propagation network and radial basis network into the research of the system-level power management policies. We proposed two PM policies-Back propagation Power Management (BPPM) and Radial Basis Function Power Management (RBFPM) which are based on Artificial Neural Networks (ANN). Our experiments show that the two power management policies greatly lowered the system-level power consumption and have higher performance than traditional Power Management(PM) techniques-BPPM is 1.09-competitive and RBFPM is 1.08-competitive vs. 1.79 . 1.45 . 1.18-competitive separately for traditional timeout PM . adaptive predictive PM and stochastic PM.

A DSWN-based specific-purpose neural computing system

The Double Synapse Weighted Neuron (DSWN) is a kind of general-purpose neuron model, which with the ability of configuring Hyper-sausage neuron (HSN). After introducing the design method of hardware DSWN synapse, this paper proposed a DSWN-based specific purpose neural computing device-CASSANN-IIspr. As its application, a rigid body recognition system was developed on CASSANN-IIspr, which achieved better performance than RIBF-SVMs system.

Hypersausage neural networks and its application in face recognition

In this paper, we firstly give the nature of 'hypersausages', study its structure and training of the network, then discuss the nature of it by way of experimenting with ORL face database, and finally, verify its unsurpassable advantages compared with other means.

Circuit design and simulation of a CMOS-based preamplifier for brain neural signals

A novel CMOS-based preamplifier for amplifying brain neural signal obtained by scalp electrodes in brain-computer interface (BCI) is presented in this paper. By means of constructing effective equivalent input circuit structure of the preamplifier, two capacitors of 5 pF are included to realize the DC suppression compared to conventional preamplifiers. Then this preamplifier is designed and simulated using the standard 0.6 mu m MOS process technology model parameters with a supply voltage of 5 volts. With differential input structures adopted, simulation results of the preamplifier show that the input impedance amounts to more than 2 Gohm with brain neural signal frequency of 0.5 Hz-100 Hz. The equivalent input noise voltage is 18 nV/Hz(1/2). The common mode rejection ratio (CMRR) of 112 dB and the open-loop differential gain of 90 dB are achieved.

on string languages generated by spiking neural p systems

We continue the study of spiking neural P systems by considering these computing devices as binary string generators: the set of spike trains of halting computations of a given system constitutes the language generated by that system. Although the "direct" generative capacity of spiking neural P systems is rather restricted (some very simple languages cannot be generated in this framework), regular languages are inverse-morphic images of languages of finite spiking neural P systems, and recursively enumerable languages are projections of inverse-morphic images of languages generated by spiking neural P systems.

Kinetic Model Study on Enzymatic Hydrolysis of Cellulose Using Artificial Neural Networks

Enzymatic hydrolysis of cellulose was highly complex because of the unclear enzymatic mechanism and many factors that affect the heterogeneous system. Therefore, it is difficult to build a theoretical model to study cellulose hydrolysis by cellulase. Artificial neural network (ANN) was used to simulate and predict this enzymatic reaction and compared with the response surface model (RSM). The independent variables were cellulase amount X-1, substrate concentration X-2, and reaction time X-3, and the response variables were reducing sugar concentration Y-1 and transformation rate of the raw material Y-2. The experimental results showed that ANN was much more suitable for studying the kinetics of the enzymatic hydrolysis than RSM. During the simulation process, relative errors produced by the ANN model were apparently smaller than that by RSM except one and the central experimental points. During the prediction process, values produced by the ANN model were much closer to the experimental values than that produced by RSM. These showed that ANN is a persuasive tool that can be used for studying the kinetics of cellulose hydrolysis catalyzed by cellulase.

人胚胎干细胞建系及向神经细胞定向分化的研究

研究和利用人胚胎干细胞(hES)细胞已成为生命科学领域的核心问题之一。当前hES 细胞研究主要集中在hES 的建系和维持其不分化状态；提高hES 细胞定向分化为特定 细胞的比例；ES 细胞自我更新和分化的机制等方面。本论文一方面概述了hES 细胞相 关领域的研究进展；另一方面建立了不同培养体系条件下3 株hES 细胞，并在此基础 上利用G5 和肝生长因子（HGF）诱导hES 细胞定向分化成高纯度的NPs。主要结论如 下：1) 建立人卵体外受精和胚胎培养体系。获得了15 个囊胚，采用了免疫外科法分离 内细胞团，运用含血清以及不含血清的培养体系，在ICR 小鼠胚胎成纤维饲养层上分 别建立了YKh-1、YKh-2 和YKh-3 3 株人胚胎干细胞系，生长良好，核型正常。ES 细 胞表达碱性磷酸酶活性、SSEA-3、SSEA-4、TRA-1-60、TRA-1-81 和Oct-4，但不表达 SSEA-1； ES 细胞在体外能够分化为属于外胚层、中胚层和内胚层的各种分化细胞， 在SCID 小鼠体内能形成畸胎瘤，畸胎瘤包括了所有三个胚层来源的细胞类型。证实了 ES 细胞系的多向分化潜能。2) 对比含血清以及无血清的培养体系的hES 细胞系的特征， 观察了其集落形态、生长速度、分化能力。结果表明，在含血清培养体系的Yhk-2，其 集落形态较致密，含2-3 个核的细胞较多，细胞倍增时间为43.9±5.7h；而在无血清培 养体系的Yhk-3，其集落形态较铺展，细胞较小而圆，倍增时间为34.8±3.8h。细胞免疫 染色和PCR 结果表明，二者在体外都能分化为三个胚层来源的多种细胞，但比例有所 差异。提示二者在向三个胚层来源的细胞的分化能力上有所不同。 3) 以所建立的hES 细胞系为模型，采用HGF 和G5 作为诱导因子添加到神经诱导培养基中，诱导hES 细 胞分化成高纯度的NPs。单独的HGF 或G5 仅能诱导ES 细胞分化成70.9± 5.0%和 72.9±7.2%NPs，而联用HGF 和G5 使NPs 的比率达到91.2±11.2%，进一步纯化后获得 98±3.2%的NPs。获得的NPs 能分化成三个谱系神经细胞，亚克隆实验也进一步证明采 用HGF+G5 获得的单个NPs 具有神经干细胞的特性，也能在体外分化成三个谱系的神 经细胞。用SHH 处理NPs，获得的分化细胞表达不同脑区标志，表明所得到NPs 具有 对脑区信号发生反应，进一步分化为不同脑区神经元细胞的能力。 本实验建立了具有自主知识产权的中国人源胚胎干细胞系，建立了ES 细胞的含血 清以及无血清的培养体系和向神经前体细胞定向分化系统，得到高比例的神经前体细 胞，为进一步研究利用人胚胎干细胞打下良好的基础。

人胚胎干细胞定向分化为神经前体细胞

人胚胎干细胞(human embryonic stem cells, hES细胞)来源于植入前胚胎的内细胞团，具有自我更新能力和发育全能性，能够在体内外分化为代表三个胚层的细胞类型。hES细胞来源的神经前体细胞（neural progenitor）对于研究胚胎早期的神经发育以及药物筛选和神经系统疾病的细胞替代性治疗具有重要意义。然而，许多因素影响了ES细胞的临床应用，如供体细胞不足、纯度低、异源物质污染等。 本研究采用同源饲养层培养的hES细胞在单层培养条件下高效地分化得到了神经前体细胞。主要结论如下：1）hES细胞在同源饲养层HAFi上培养八个月后仍保持ES细胞的各项表型特征和抗原特性。表明HAFi能够支持hES细胞的长期培养，从培养条件上避免了异源物质污染的可能性。2）单层贴壁分化的方法培养成分简单，不含血清和条件培养基，不需繁琐的筛选步骤就可以得到高比例的神经前体细胞（97.5%±0.83%）（P＜0.05）。此外，成分确定的培养基是研究神经分化的分子机制的良好模型。3）hES细胞来源的神经前体细胞具有分化为神经元，星形胶质和少突胶质细胞的能力，并能够模拟体内神经发育的过程和分子表达模式。长期的传代培养中发现，随着培养时间的延长，nestin阳性的神经前体细胞比例下降，同时发育能力也发生了变化。在传代培养的早期，神经前体细胞发育为神经元的比例很高，几乎没有胶质细胞分化出来。随培养时间的延长，胶质细胞的比例逐渐上升。进一步研究发现具有bHLH (basic helix-loop-helix) 结构域的转录因子neurogenein2(Ngn2) 和olig2可能在这一变化中发挥了重要的作用。

Fabrication of a Silicon-Based Microprobe for Neural Interface Applications

A two-dimensional (2D) multi-channel silicon-based microelectrode array is developed for recording neural signals. Three photolithographic masks are utilized in the fabrication process. SEM images show that the microprobe is 1. 2mm long,100μm wide,and 30μm thick, with recording sites spaced 200μm apart for good signal isolation. For the individual recording sites, the characteristics of impedance versus frequency are shown by in vitro testing. The impedance declines from 14MΩ to 1.9kv as the frequency changes from 0 to 10MHz. A compatible PCB (print circuit board) aids in the less troublesome implantation and stabilization of the microprobe.